GPU Computing Gems Emerald Edition (Applications of GPU Computing Series) (Repost)

ISBN: 0123849888

Category: Technical

Tag: Programming


Posted on 2011-09-08. By anonymous.

Description


GPU Computing Gems Emerald Edition (Applications of GPU Computing Series) By Wen-mei W. Hwu
Publisher: Morgan Kaufmann 2011 | 886 Pages | ISBN: 0123849888 | PDF | 19 MB



"...the perfect companion to Programming Massively Parallel Processors by Hwu & Kirk." -Nicolas Pinto, Research Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010
Graphics processing units (GPUs) can do much more than render graphics. Scientists and researchers increasingly look to GPUs to improve the efficiency and performance of computationally-intensive experiments across a range of disciplines.
GPU Computing Gems: Emerald Edition brings their techniques to you, showcasing GPU-based solutions including:
Black hole simulations with CUDA
GPU-accelerated computation and interactive display of molecular orbitals
Temporal data mining for neuroscience
GPU -based parallelization for fast circuit optimization
Fast graph cuts for computer vision
Real-time stereo on GPGPU using progressive multi-resolution adaptive windows
GPU image demosaicing
Tomographic image reconstruction from unordered lines with CUDA
Medical image processing using GPU -accelerated ITK image filters
41 more chapters of innovative GPU computing ideas, written to be accessible to researchers from any domain
GPU Computing Gems: Emerald Edition is the first volume in Morgan Kaufmann's Applications of GPU Computing Series, offering the latest insights and research in computer vision, electronic design automation, emerging data-intensive applications, life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, and video / image processing.


Covers the breadth of industry from scientific simulation and electronic design automation to audio / video processing, medical imaging, computer vision, and more

Many examples leverage NVIDIA's CUDA parallel computing architecture, the most widely-adopted massively parallel programming solution

Offers insights and ideas as well as practical "hands-on" skills you can immediately put to use

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